langchain-agent-setup

Build a Gemini-powered LangChain agent by configuring the GeminiLLM wrapper, defining system prompts, and adding new capabilities as methods on the class. An InterviewOrchestrator then wires these methods together to manage the end-to-end flow.

An orchestrator separates LLM initialization from capability execution by routing tasks to methods defined on GeminiLLM. This modular approach enforces a clean separation of concerns between system prompts, capability extension, and front-end or back-end flow.

Add new capabilities to a LangChain AI agent by implementing them as methods directly on the GeminiLLM class. You then hook these methods into the orchestrator, maintaining a testable architecture while extending the assistant's functionality.

Customize system prompts for different tasks in a LangChain agent using a centralized pattern. This allows you to switch system prompts based on the specific session type or task, ensuring deterministic responses from the Gemini model.

This LangChain agent setup does not require external dependencies. It provides configuration and extension patterns using the GeminiLLM wrapper and InterviewOrchestrator within your existing software engineering environment.

Use a modular architecture for LLM-driven interview assistants to enforce a clean separation of concerns between LLM wiring, capability orchestration, and front-end flow. This ensures the assistant remains testable and extensible as new features are added.